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Record W3145722221 · doi:10.1109/aspdac.2010.5419927

A PUF design for secure FPGA-based embedded systems

2010· article· en· W3145722221 on OpenAlexaff
Jason H. Anderson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicPhysical Unclonable Functions (PUFs) and Hardware Security
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsField-programmable gate arrayEmbedded systemRouting (electronic design automation)Computer scienceSignature (topology)Process (computing)Physical unclonable functionIntegrated circuitDigital signatureLogic synthesisComputer architectureIntegrated circuit designVirtexComputer hardwareLogic gateArbiterOperating system

Abstract

fetched live from OpenAlex

The concept of having an integrated circuit (IC) generate its own unique digital signature has broad application in areas such as embedded systems security, and IP/IC counter-piracy. Physically unclonable functions (PUFs) are circuits that compute a unique signature for a given IC based on the process variations inherent in the IC manufacturing process. This paper presents the first PUF design specifically targeted for field-programmable gate arrays (FPGAs). Our novel design makes use of the underlying FPGA architecture, and unlike prior published PUFs, the proposed PUF can be naturally embedded into a design's HDL, consuming very little area, and does not require the use of "hard macros" with fixed routing. Measured results on the Xilinx Virtex-5 65 nm FPGA demonstrate PUF signatures to be both unique and reliable under temperature variation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.244
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations122
Published2010
Admission routes1
Has abstractyes

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